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Figure 14 in Character-matrix based descriptions of two new nemertean (Nemertea) species
Figure 14. Schematic representation of the proboscis apparatus showing proboscis nerves. Abbreviations: P, proboscis; PN, proboscis nerves; PNS, peripheral neural sheath; R, rhynchocoel; SPN, secondary proboscis nerves.
Figure 15 in Character-matrix based descriptions of two new nemertean (Nemertea) species
Figure 15. Schematic view of transverse section through the intestinal region of a nemertean to show the various organ systems mentioned in the character matrix list. The dashed lines show distribution of intestinal diverticulae alternating with the reproductive system. Abbreviations: Layers and structures of the body wall: E, epidermis; D, dermis; CM, circular muscle layer; LM, longitudinal muscle layer; PA, parenchyma. Proboscis apparatus: R, rhynchocoel; PM, proboscis retractor muscle. Alimentary system: I, intestine; IE, intestinal epithelium; SID, shallow intestinal diverticula; DID, deep intestinal diverticula. LN, lateral nerve cord; LV, lateral blood vessel. Reproductive system: G, gonad; O, ovary; OC, ovocyte; N, nucleolus; GD, gonoduct; GP, gonopore.
Figure 10 in Character-matrix based descriptions of two new nemertean (Nemertea) species
Figure 10. Schematic views of different cephalic furrows. A, ventral transversal furrows; B, small arches; C, posterior transversal furrow; D, lateral horizontal furrows; E, V-shaped furrows with secondary grooves (ghosted furrows show ventral distribution). See also Fig. 8.
Figure 9 in Character-matrix based descriptions of two new nemertean (Nemertea) species
Figure 9. Nemertean head shapes, cephalic furrows, and eye distribution. A, Nemertopsis flavida with two pairs, anterior and posterior, cephalic furrows and eyes in rectangle; B, Oerstedia dorsalis with eyes in square; C, Callinera monensis; D, Amphiporus bioculatus with anterior V-shaped and posterior L-shaped cephalic furrows. However, different authors have described the furrows of this species in other ways (McIntosh, 1873–74). Also note the two eyes at the anterior tip of the head; E, Tetrastemma cephalophorum; F, general representation of truncated head shape; G, Tubulanus inexpectatus; H, Amphiporus dissimulans with anterior and posterior U-shaped cephalic furrows. Also note the eyes arranged in lateral rows on each side of head; I, Nipponnemertes pulchra with secondary grooves of anterior cephalic furrows. Eyes as H; J, Tetrastemma flavidum; K, Prosorhochmus claparedii, note the distinct anterior eyes. (Redrawn from Gibson, 1974.)
Figure 7 in Character-matrix based descriptions of two new nemertean (Nemertea) species
Figure 7. Phylogeny of a selected number of palaeonemertean taxa to show the position of the new species described here (in bold). Majority rule consensus tree from a Bayesian analysis based on COI mtDNA sequences. Numbers above branches refer to a posteriori probabilities (only values> 95% reported). Outgroup species is Emplectonema neesii.
Figure 6. A–E in Character-matrix based descriptions of two new nemertean (Nemertea) species
Figure 6. A–E. Carinina ochracea sp. nov. A, section through cephalic region showing cerebral ganglion and intra-epithelial muscle fibre network. B, section through stomach region showing proboscis and body wall muscle layers. Note the rhynchocoelic villus (arrowhead). C, section through intestinal region showing rhynchocoelic caecum (arrowhead). D, section through intestinal region showing the excretory system. E, section through gonad region of body showing intestine and ova with nucleus (arrowhead). Abbreviations: CG, cerebral ganglion; CM, outer circular muscle layer; D, dermis; E, epidermis; F, follicle; GD, gonoduct; I, intestine; ICM, inner circular muscle; IEMN, intra-epithelial muscle fibre network; LM, longitudinal muscles; LN, lateral nerve cord; LV, lateral blood vessel; MF, muscle fibre; OV, ovum; P, proboscis; PN, protonephridia; R, rhynchocoel; RY, rhynchodaeum; S, stomach.
Figure 4. A–F in Character-matrix based descriptions of two new nemertean (Nemertea) species
Figure 4. A–F, Raygibsonia bergi gen. et sp. nov. A, section through anterior-most part of body showing cephalic glands, cerebral organs, oesophagus, and the poorly developed precerebral septum. B, section through cephalic region showing cephalic blood supply, stomach, and cerebral ganglia. Note the vascular plug (arrowhead). C, section through pyloric region showing the connection between pylorus and intestine. Note the myofibrillae in the lateral nerve cord (arrowhead) and the nerves of the proboscis (arrow). D, section through proboscis armature region of body showing the stylet apparatus. Note the accessory stylet (arrow) and the peripheral neural sheath (arrowhead) that connect the proboscis nerves. E, section through gonad region of body showing immature gonads (probably sac-like). Note the accessory lateral nerve (arrowhead). F, section through stomach region of body, showing distribution of gland cells. Abbreviations: AP, accessory stylet pouch; AN, accessory lateral nerve; CB, cerebral ganglion; CC, cerebral canal; CF, cephalic furrow; CG, cephalic glands; CL, dorsal lobules of cephalic glands; CM, circular muscle layer; CO, cerebral organ; D, dermis; DC, dorsal commissure; DG, dorsal ganglion; E, epidermis; ECF, cephalic gland encircling the foregut; G, gonad; I, intestine; IC, intestinal caecum; LD, lateral diverticula; LM, longitudinal muscle layer; LN, lateral nerve cord; LV, lateral blood vessel; MDV, mid-dorsal vessel; OE, oesophagus; P, proboscis; PE, proboscis epithelium; PN, proboscis nerve; PS, precerebral septum; PY, pylorus; R, rhynchocoel; RM, retractor muscle; S, stomach; SB, stylet basis; VG, ventral ganglion.
Figure 3 in Character-matrix based descriptions of two new nemertean (Nemertea) species
Figure 3. Raygibsonia bergi gen. et sp. nov. Drawing by Helena Samuelsson, The Encyclopedia of the Swedish Flora and Fauna.
Figure 16 in Character-matrix based descriptions of two new nemertean (Nemertea) species
Figure 16. General scheme of the transverse section through the brain region of a nemertean to show the various organs and tissues mentioned in the character matrix. Abbreviations: Layers and structures of the body wall. E, epidermis; D, dermis; CM, circular muscle layer; LM, longitudinal muscle layer; CF, cephalic furrow. Nervous system and sensory organs: DG, dorsal ganglion; VG, ventral ganglion; VC, ventral commissure; DC, dorsal commissure; NC, neurochord cell; ST, statocyst; CO, cerebral organ; CC, ciliated cerebral canal; FCC, forked cerebral canal; CG, dorsolateral extensions of cephalic glands; CL, cephalic lacuna; S, stomach.
Figure 1 in Character-matrix based descriptions of two new nemertean (Nemertea) species
Figure 1. Phylogeny of a selected number of nemertean taxa to show the position of the two new species described here (in bold). Majority rule consensus tree from a Bayesian analysis based on 18S rDNA sequences. Numbers above branches refer to a posteriori probabilities (only values> 95% reported). Three phoronid species were used as outgroup: Phoronis vancouverensis, Phoronis australis, Phoronis hippocrepia.
Figure 2 in Character-matrix based descriptions of two new nemertean (Nemertea) species
Figure 2. Phylogeny of a selected number of hoplonemertean taxa to show the position of the new hoplonemertean species described here (in bold). Majority rule consensus tree from a Bayesian analysis based on COI mtDNA sequences. Numbers above branches refer to a posteriori probabilities (only values> 95% reported). Outgroup species is Reptant nemertean sp500 (from Thollesson & Norenburg, 2003).
Colombia researchers co-authorship network and adjacency matrix.
<p>OpenAlex subset of papers published by researchers affiliated with an institution in Colombia.</p> <p>* gml file containing the coauthorship network with the metadata: Researcher name, one affiliation, list of OpenAlex concepts of level 0.</p> <p>* Adjacency matrix of type scipy sparse saved in a npz file</p> <p>*npz file with the concepts the author ar marked with</p> <p>*npz file with a map of 200 words and the affiliation each author reports</p> <p>*npz file with a dict that has the mappings from numbers to string for each variable (X with words and affiliations and Z with concepts)</p>
Datasets for the paper: "Rock-matrix porosity and permeability of the hydrothermally altered, upper oceanic crust, Oman ophiolite"
<p>This dataset includes all the data used for the paper submitted to the Journal of Geophysical Research: Solid Earth, entitled: <strong>Rock-matrix porosity and permeability in the hydrothermally altered, upper oceanic crust, Oman ophiolite. </strong></p> <p>One excel file is provided with the following tables on separate sheets:</p> <p>Table 1: Sample locations, alteration types, and petrophysical analyses</p> <p>Table S1: Trace element Zirconium and Yttrium analysis for this study’s sample set in Figure S1</p> <p>Table S2: Outcrop upscale results from image analysis</p> <p>Table S3: Sample bulk rock geochemistry</p>
A Water-Focused Social Accounting Matrix for Jordan
<p>The 2015 Water-Focused Social Accounting Matrix (WF-SAM) for Jordan represents a significant enhancement over the existing SAM introduced by (Raouf et al., 2021). This enhancement lies in its comprehensive integration of the water sector at the micro-account level. Constructed with thorough attention to detail, the 2015 WF-SAM for Jordan draws upon a triad of distinct data sources. <em>Firstly</em>, it incorporates micro-account data derived from the foundational SAM developed by Raouf et al. in 2021, acknowledged as the most contemporary SAM for Jordan, benefiting from current national and international data sources. <em>Secondly</em>, a contribution stems from the 2017 reports of the General Budget Department (GBD), furnishing actual financial allocations of Jordanian water utilities (General Budget Department, 2017a, 2017f, 2017e, 2017d, 2017c, 2017b). Adapting these allocations into novel account totals and deficits within the WF-SAM framework amplifies its comprehensiveness. The <em>third</em> data source comes from (MWI, 2015)and (Salamah, 2021), disclosing the nuanced cash flow interplay between the water-centric accounts and the entire economy, for instance, the municipal water sector allocates 12% of its total expenditures to the electricity commodity, establishing a symbiotic relationship. This calibrated interdependence of sectors elevates the precision and disaggregation of the 2015 WF-SAM for Jordan.</p> <p>Out of the convention in this documentation, the 2015 JO SAM by (Raouf et al., 2021) is used in constructing proto-SAM and is identified as "Base-SAM." To enhance and modernize the representation of the water sector within the Base-SAM, budgetary components sourced from audited national public budget documents were carefully correlated with corresponding accounts within the SAM structure, adhering to the established ISIC standard economic classifications. This alignment resulted in the creation of a preliminary SAM, although it entailed further refinement to achieve equilibrium. Employing the cross-entropy SAM estimation algorithm, as set forth by (Robinson et al., 1998)), facilitated the fulfilment of the ultimate balanced state. The resultant Water-Focused Social Accounting Matrix (WF-SAM) is enclosed within the attached Excel workbook.</p> <p>The WF-SAM for Jordan, includes 155 accounts, 57 activities, 63 commodities, one transaction cost account, 13-factor accounts, ten household accounts plus one enterprise account, and seven government-related accounts: one for the government itself and six for direct and indirect taxes and subsidies, with one saving and investment account and one currency exchange account, the rest of the world is represented in one account,” RoW.” The water sector is represented in three commodity accounts, two activity accounts, and one water subsidies account.</p> <p>This dataset (WF-SAM) enables future research on an array of scenarios and shocks to assess policymakers in the ex-ante assessment of policy options and market conditions. The findings of this study provide valuable insights into the interplay between the water sector and the Jordanian economy and can inform policy decisions related to water resource management and development.</p>
Matrix Approach to Accelerate Spin-Up of CLM5
<p>The spin-up problem hinders our ability to study some key issues in land carbon cycle modeling, such as sensitivity analysis. This work applies a new semi-analytical spin-up (SASU) framework to accelerate spin-up of Community Land Model matrix version 5. We evaluate the computational efficiency and steady-state consistency of ND, AD and SASU approaches using CLM5 at both one-site (Brazil) and the global scale. In addition, we test SASU in Parameter Perturbation Experiment (PPE) in CLM5.</p>
Renewables Projection with Storage in the Brazilian Electricity Matrix using OSeMOSYS and Flextool - Dataset
<p>This dataset presents the input and results for the study on the increase of Solar and Wind energy generation in the Brazilian Power System, as a way to achieve the NetZero by 2050. This study was conducted using the OSeMOSYS and Flextool as a deliverable of the Energy Modelling Platform for Latin America and The Caribbean Course - EMP-LAC 2023.</p>
Structure of the complete review matrix developed in the context of the "Reviewing Computational Thinking in Compulsory Education: State of Play and Practices from the Field" research study
<p>This is the structure of the complete review matrix used to analyse in-depth <strong>98 selected publications from between 2016 and 2021 </strong>in the context of the "<a href="https://publications.jrc.ec.europa.eu/repository/handle/JRC128347">Reviewing Computational Thinking in Compulsory Education: State of Play and</a> <a href="https://publications.jrc.ec.europa.eu/repository/handle/JRC128347"> Practices from the Field</a>" research study. The <a href="https://computhink2study.eu/">study </a>was designed, funded, and followed by the European Commission’s Joint Research Centre (JRC) to investigate <strong>how Computational Thinking (CT) is currently positioned within compulsory school education in Europe’s various Member States, as well as outside the EU</strong>. The study was carried out from April to December 2021 by the Institute for Educational Technology of the Italian National Research Council (CNR-ITD), together with European Schoolnet (EUN) and Vilnius University (VU).</p>
AHEAD2020 WP9.8 Soft Proton Response Matrix
<p>Low energy (< 300 keV) protons entering the field of view of the XMM-Newton telescope and scattering with the mirror surface are observed in the form of a sudden increase in the background level. Such flaring events, affecting about 30-40% of the XMM-Newton observing time, can hardly be disentangled from true X-ray events and cannot be rejected on board. All future high throughput gracing incidence X-ray telescopes operating outside the radiation belts (e.g. ATHENA) are potentially affected by soft proton induced contamination that must be foreseen and limited since the design phase. On the other side, a clear description of the interaction model would link the observed soft proton spectra by XMM-Newton to the ones hitting the telescope pupil, mapping the low energy particle environment along its orbit.</p> <p>Thanks to the latest validation studies on the physics models describing the reflection process of protons at grazing angles, we build a proton response matrix for the XMM-Newton and ATHENA missions, describing the effective area and energy redistribution of protons entering the mirror aperture. The simulation pipeline comprises two independent simulation frameworks for the X-ray optics reflectivity, based on ray-tracing and Geant4, and a Geant4 simulation for the proton transmission efficiency caused by the combination of optical filters, on-chip electrodes and the detection depletion regions, requiring a detailed mass model of the focal plane assemblies.</p> <p>The response matrix for protons will allow a better understanding of the proton radiation environment, with the aim of modeling the in-flight non X-ray background of current and future X-ray focusing telescopes. The XMM-Newton matrix will be used to analyze the mean energy spectra of the background flares, converting the mission into a “proton telescope”, while characterizing its particle background. The matrices for the ATHENA telescope will allow for a fast evaluation of the soft proton induced background for any input population, driving the design of shielding solutions.</p> <p>The response matrix is formatted according to the NASA OGIP (Office of Guest Investigators Program) calibration database (caldb) format, and it consists of an RMF and ARF file in FITS (Flexible Image Transport System) format. X-ray data analysis tools available to the X-ray astronomy community such as Xspec and SPEX can be used to simulate or analyse the soft proton-induced background spectra.</p> <p>The research leading to these results has received funding from the European Union’s Horizon 2020 Programme under the AHEAD2020 project (grant agreement n. 871158).</p>
Performance measurements for "Bringing Order to Sparsity: A Sparse Matrix Reordering Study on Multicore CPUs"
<p>The paper "Bringing Order to Sparsity: A Sparse Matrix Reordering Study on Multicore CPUs" compares various strategies for reordering sparse matrices. The purpose of reordering is to improve performance of sparse matrix operations, for example, by reducing fill-in resulting from sparse Cholesky factorisation or improving data locality in sparse matrix-vector multiplication (SpMV). Many reordering strategies have been proposed in the literature and the current paper provides a thorough comparison of several of the most popular methods.</p> <p>This comparison is based on performance measurements that were collected on the eX3 cluster, a Norwegian, experimental research infrastructure for exploration of exascale computing. These performance measurements are gathered in the data set provided here, particularly related to the performance of two SpMV kernels with respect to 490 sparse matrices, 6 matrix orderings and 8 multicore CPUs.</p> <p>Experimental results are provided in a human-readable, tabular format using plain-text ASCII. This format may be readily consumed by gnuplot to create plots or imported into commonly used spreadsheet tools for further analysis.</p> <p>Performance measurements are provided based on an SpMV kernel using the compressed sparse row (CSR) storage format with 7 matrix orderings. One file is provided for each of 8 multicore CPU systems considered in the paper:</p> <p> 1. Skylake: csr_all_xeongold16q_032_threads_ss490.txt<br> 2. Ice Lake: csr_all_habanaq_072_threads_ss490.txt<br> 3. Naples: csr_all_defq_064_threads_ss490.txt<br> 4. Rome: csr_all_rome16q_016_threads_ss490.txt<br> 5. Milan A: csr_all_fpgaq_048_threads_ss490.txt<br> 6. Milan B: csr_all_milanq_128_threads_ss490.txt<br> 7. TX2: csr_all_armq_064_threads_ss490.txt<br> 8. Hi1620: csr_all_huaq_128_threads_ss490.txt</p> <p>A corresponding set of files and performance measurements are provided for a second SpMV kernel that is also studied in the paper.</p> <p>Each file consists of 490 rows and 54 columns. Each row corresponds to a different matrix from the SuiteSparse Matrix Collection (https://sparse.tamu.edu/). The first 5 columns specify some general information about the matrix, such as its group and name, as well as the number of rows, columns and nonzeros. Column 6 specifies the number of threads used for the experiment (which depends on the CPU). The remaining columns are grouped according to the 7 different matrix orderings that were studied, in the following order: original, Reverse Cuthill-McKee (RCM), Nested Dissection (ND), Approximate Minimum Degree (AMD), Graph Partitioning (GP), Hypergraph Partitioning (HP), and Gray ordering. For each ordering, the following 7 columns are given:</p> <p><br> 1. Minimum number of nonzeros processed by any thread by the SpMV kernel<br> 2. Maximum number of nonzeros processed by any thread by the SpMV kernel<br> 3. Mean number of nonzeros processed per thread by the SpMV kernel<br> 4. Imbalance factor, which is the ratio of the maximum to the mean number of nonzeros processed per thread by the SpMV kernel<br> 5. Time (in seconds) to perform a single SpMV iteration; this was measured by taking the minimum out of 100 SpMV iterations performed<br> 6. Maximum performance (in Gflop/s) for a single SpMV iteration; this was measured by taking twice the number of matrix nonzeros and dividing by the minimum time out of 100 SpMV iterations performed.<br> 7. Mean performance (in Gflop/s) for a single SpMV iteration; this was measured by taking twice the number of matrix nonzeros and dividing by the mean time of the 97 last SpMV iterations performed (i.e., the first 3 SpMV iterations are ignored).</p> <p>The results in Fig. 1 of the paper show speedup (or slowdown) resulting from reordering with respect to 3 reorderings and 3 selected matrices. These results can be reproduced by inspecting the performance results that were collected on the Milan B and Ice Lake systems for the three matrices Freescale/Freescale2, SNAP/com-Amazon and GenBank/kmer_V1r. Specifically, the numbers displayed in the figure are obtained by dividing the maximum performance measured for the respective orderings (i.e., RCM, ND and GP) by the maximum performance measured for the original ordering.</p> <p>The results presented in Figs. 2 and 3 of the paper show the speedup of SpMV as a result of reordering for the two SpMV kernels considered in the paper. In this case, gnuplot scripts are provided to reproduce the figures from the data files described above.</p>
Data for Coupling Covariance Matrix Adaptation with Continuum Modeling for Determination of Kinetic Parameters Associated with Electrochemical CO2 Reduction
<p>This data set contains digitized and tagged polarization and partial current density data for 18 datasets of CO<sub>2</sub> reduction to H<sub>2</sub> and CO over Ag catalysts, as well as 8 datasets of CO<sub>2</sub> reduction to HCOO<sup>-</sup>, CO, and H<sub>2</sub> over Sn catalysts. We analyze this data using a coupled continuum modeling and covariance matrix adaptation approach for which the codebase is provided in DOI: 10.5281/zenodo.7866195.</p>
ScienceDex guides
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These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.